Directional statistics visualization tool
a visualization tool and diffusion tensor technology, applied in the field of diffusion tensor imaging, can solve the problems of obscuring the information intended to be conveyed, visual clutter, and computational cost of techniques
Inactive Publication Date: 2011-02-15
SIEMENS MEDICAL SOLUTIONS USA INC
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Benefits of technology
The present patent is about a computer-implemented method for visualizing diffusion tensor images. The method involves providing a diffusion tensor image input and a volume of interest within it. The method then determines a plurality of direction-based classifications of the volume of interest using a set of parameters. An optimal solution is then selected within the classifications using a criterion. To represent the chosen classification, a cone graph is determined for each directional class. A spherical scatterplot of the volume of interest is then determined, which is augmented with a cone graph for visualization of at least one directional class. The technical effect of this patent is to provide a more efficient and effective way of visualizing diffusion tensor images.
Problems solved by technology
This type of visualization can become visually cluttered, obscuring the information intended to be conveyed.
These techniques can be computationally expensive.
Method used
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[0021]According to an embodiment of the present disclosure, a visualization tool for direction data augments a spherical visualization of diffusion tensor voxels with directional information using cone graphs.
[0022]One way to represent diffusion tensor data is to display a scatterplot of unit vectors on a 3 dimensional sphere (for example, see FIG. 1A). However, this approach has a limitation: as the number of dots grows bigger, it becomes harder to distinguish main directions from noise.
[0023]This problem appears in DTI frameworks: when a user defines a Volume of Interest (VOI), a main direction is determined and plotted for each voxel that is selected. If many voxels are selected, this will result in a scatterplot with so many dots that it becomes substantially impossible to distinguish significant clusters (see for example, FIG. 1B). According to an embodiment of the present disclosure, problem was approached by sorting vectors into a small number of classes. Each class can then ...
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A computer-implemented method for visualization of diffusion tensor images includes providing a diffusion tensor image input and providing a volume of interest within the diffusion tensor image input. The method includes determining a plurality of direction-based classifications of the volume of interest, wherein classes are defined by a set of parameters, An optimal solution is then selected within the classifications by using a criterion defined as a ratio of inertia indicators. To represent the chosen classification, a cone graph is determined for each of the directional classes to be displayed or stored, each cone pair being the geometrical interpretation of the class parameters. The method further includes determining a spherical scatterplot of the volume of interest augmented with a cone graph for visualization of at least one of the directional classes, and displaying and / or storing the scatterplot.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit of Provisional Application No. 60 / 798,355 filed on May 5, 2006 in the United States Patent and Trademark Office, the content of which is herein incorporated by reference in its entirety.BACKGROUND OF THE INVENTION[0002]1. Technical Field[0003]The present disclosure relates to diffusion tensor imaging, and more particularly to directional statistics visualization for diffusion tensor imaging (DTI).[0004]2. Description of Related Art[0005]Diffusion tensor image scans comprise at least six gradient directions, sufficient to determine a diffusion tensor in, for example, a brain scan. From the diffusion tensor, diffusion anisotropy measures such as the Fractional Anisotropy (FA) can be determined. Moreover, the principal direction of the diffusion tensor can be used to infer white-matter connectivity of the brain and model it as a tract.[0006]A visualization strategy for such a tract is to render a diffusion...
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IPC IPC(8): G06K9/00G06K9/46G06K9/62G01V3/00A61B5/05
CPCG01R33/56341G01R33/5608G06T2207/10092
Inventor NADAR, MARIAPPAN S.FLIPO, AURELIEN
Owner SIEMENS MEDICAL SOLUTIONS USA INC